Jiahao Chen jiahao

## savitzkygolay.jl
"""
Savitzky-Golay filter of window half-width M and degree N

M is the number of points before and after to interpolate, i.e. the full width
of the window is 2M+1
"""
immutable SavitzkyGolayFilter{M,N} end

@generated function Base.call{M,N,T}(::Type{SavitzkyGolayFilter{M,N}},
     data::AbstractVector{T})

## llama2.jl
# A port of https://github.com/karpathy/llama2.c/blob/master/run.c
# to Julia.
# Jiahao Chen <jiahao@csail.mit.edu> 2023-07-29
#
# MIT License: see full text at https://opensource.org/license/mit/
#


using LinearAlgebra
using LogExpFunctions

## doubledescent.jl
using Plots
using StatsPlots
using LinearAlgebra
using ClassicalOrthogonalPolynomials
using ProgressMeter
using Statistics

k = 15 # Size of training data
l = 15 # Size of test data

## nf4.jl
using Statistics
using BFloat16s
using StaticArrays

import Base: getindex, setindex!, length, iterate

###########################################
# Implementation of the NormedFloat4 type
# and its container type, QLoRAArray
#

## nrules.svg

      
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                What do .gitignore files tell us about the inherent complexity of programming languages? This gist can be run on data from GitHub's own collection of gitignore templates, whose repository is at https://github.com/github/gitignore, and counts how many rules are present in the template for each programming language.
              
          
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## setup_julia.sh
#Set up data partition
sudo mkdir /data
sudo chmod 777 /data
sudo "echo /dev/xvdb   /data	ext4	rw,user,exec,comment=cloudconfig	0	2 >> /etc/fstab"
sudo mount /data

#Install build environment
sudo sed -i "s/enabled=0/enabled=1" /etc/yum.repos.d/epel.epo
sudo yum -y update
sudo yum -y upgrade

## Optimizer.py
#!/usr/bin/env python

'''
Pure Python implementation of some numerical optimizers

Created on Jan 21, 2011

@author Jiahao Chen
'''

## listpdfs.py
from bs4 import BeautifulSoup
import urllib.request

url = "https://arxiv.org/a/chen_j_2.html"

with urllib.request.urlopen(url) as response:
   html = response.read()

soup = BeautifulSoup(html, 'html.parser')
for link in soup.find_all('a'):

## tsqr.jl
#################################################################
# A simple implementation of TSQR (Tall and Skinny QR) in Julia #
# Jiahao Chen <jiahao@mit.edu>, 2015-08-03                      #
#################################################################
#
# TSQR is a communication-avoiding QR algorithm designed for
# distributed computation on tall, skinny matrices, which are
# typical of data matrices in Big Data applications.
#
# The primary reference is

## BagOfLittleBootstraps.jl
using Distributions

export Measure, DiscreteMeasure, Estimator, EstimatorQuality,
       blb, randsubset

abstract Measure

type DiscreteMeasure{S<:Number,T<:Number} <: Measure
  points :: Vector{S}
  weights :: Vector{T}
	"""
	Savitzky-Golay filter of window half-width M and degree N

	M is the number of points before and after to interpolate, i.e. the full width
	of the window is 2M+1
	"""
	immutable SavitzkyGolayFilter{M,N} end

	@generated function Base.call{M,N,T}(::Type{SavitzkyGolayFilter{M,N}},
	data::AbstractVector{T})
	# A port of https://github.com/karpathy/llama2.c/blob/master/run.c
	# to Julia.
	# Jiahao Chen <jiahao@csail.mit.edu> 2023-07-29
	#
	# MIT License: see full text at https://opensource.org/license/mit/
	#


	using LinearAlgebra
	using LogExpFunctions
	using Plots
	using StatsPlots
	using LinearAlgebra
	using ClassicalOrthogonalPolynomials
	using ProgressMeter
	using Statistics

	k = 15 # Size of training data
	l = 15 # Size of test data
	using Statistics
	using BFloat16s
	using StaticArrays

	import Base: getindex, setindex!, length, iterate

	###########################################
	# Implementation of the NormedFloat4 type
	# and its container type, QLoRAArray
	#
	#Set up data partition
	sudo mkdir /data
	sudo chmod 777 /data
	sudo "echo /dev/xvdb /data ext4 rw,user,exec,comment=cloudconfig 0 2 >> /etc/fstab"
	sudo mount /data

	#Install build environment
	sudo sed -i "s/enabled=0/enabled=1" /etc/yum.repos.d/epel.epo
	sudo yum -y update
	sudo yum -y upgrade
	#!/usr/bin/env python

	'''
	Pure Python implementation of some numerical optimizers

	Created on Jan 21, 2011

	@author Jiahao Chen
	'''
	from bs4 import BeautifulSoup
	import urllib.request

	url = "https://arxiv.org/a/chen_j_2.html"

	with urllib.request.urlopen(url) as response:
	html = response.read()

	soup = BeautifulSoup(html, 'html.parser')
	for link in soup.find_all('a'):
	#################################################################
	# A simple implementation of TSQR (Tall and Skinny QR) in Julia #
	# Jiahao Chen <jiahao@mit.edu>, 2015-08-03 #
	#################################################################
	#
	# TSQR is a communication-avoiding QR algorithm designed for
	# distributed computation on tall, skinny matrices, which are
	# typical of data matrices in Big Data applications.
	#
	# The primary reference is
	using Distributions

	export Measure, DiscreteMeasure, Estimator, EstimatorQuality,
	blb, randsubset

	abstract Measure

	type DiscreteMeasure{S<:Number,T<:Number} <: Measure
	points :: Vector{S}
	weights :: Vector{T}